In this section, we will introduce our proposed weakly supervised object detection architecture. As shown in Figure REF , the first stage aims to extract enhanced feature maps \(X_5^*\)  from VGG16 with a cascade attentive dropout module (CADM) and a global context module (GCM). The enhanced feature maps and region proposals generated by Selective Search[1]} are then sent to the RoI pooling layer to produce fixed-size RoI feature maps. At last, MIL Refinement Module utilizes proposal feature vectors \(x\)  to predict object categories and locations. The proposed CADM, which is designed to elliminate negative effects of discriminative features, is employed on pooling 3 feature map. Different from ADL[21] which erased the maximally activated spatial parts, we purposely discard attentive elements in both channel and space dimension. The remainder of this section will discuss these components in detail.
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